Successful competition in a commercial enterprise often requires careful monitoring of profit margins, sales, deadlines, and many other types of business information. Businesses rely on their latest performance information to support strategic planning and decision making. Businesses without a system for providing accurate and timely forecasts of business information have large disadvantages relative to their competitors.
Accordingly, businesses often use computerized data to forecast events and outcomes, such as end-of-quarter revenue, end-of-month inventory, or end-of-year overhead costs. Forecasts are also used to monitor the probability of achieving some goal to support current business decisions. These tasks are quite challenging to model, especially in large commercial enterprises with large numbers of complex and ongoing transactions.
Some traditional methods forecast events using historical data. Such data often includes cyclic effects that provide valuable information for accurate forecasting. These cyclic effects, however, are difficult to identify, filter, and use for identifying results for analysis and forecasting.